Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach

Matsilele Mabaso, Daniel Withey, Bhekisipho Twala

Abstract

Robust spot detection in microscopy image analysis serves as a critical prerequisite in many biomedical applications. Various approaches that automatically detect spots have been proposed to improve the analysis of biological images. In this paper, we propose an approach based on Convolutional Neural Network (conv-net) that automatically detects spots using sliding-window approach. In this framework, a supervised CNN is trained to identify spots in image patches. Then, a sliding window is applied on testing images containing multiple spots where each window is sent to a CNN classifier to check if it contains a spot or not. This gives results for multiple windows which are then post-processed to remove overlaps by overlap suppression. The proposed approach was compared to two other popular conv-nets namely, GoogleNet and AlexNet using two types of synthetic images. The experimental results indicate that the proposed methodology provides fast spot detection with precision, recall and F_score values that are comparable with the other state-of-the-art pre-trained conv-nets methods. This demonstrates that, rather than training a conv-net from scratch, fine-tuned pre-trained conv-net models can be used for the task of spot detection.

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Paper Citation


in Harvard Style

Mabaso M., Withey D. and Twala B. (2018). Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING, ISBN 978-989-758-278-3, pages 67-74. DOI: 10.5220/0006724200670074


in Bibtex Style

@conference{bioimaging18,
author={Matsilele Mabaso and Daniel Withey and Bhekisipho Twala},
title={Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,},
year={2018},
pages={67-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006724200670074},
isbn={978-989-758-278-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING,
TI - Spot Detection in Microscopy Images using Convolutional Neural Network with Sliding-Window Approach
SN - 978-989-758-278-3
AU - Mabaso M.
AU - Withey D.
AU - Twala B.
PY - 2018
SP - 67
EP - 74
DO - 10.5220/0006724200670074